Learning Goals: You should know the concepts and basic mathematics of Linear Regression, Least squares optimization, overfitting/underfitting, the bias-variance tradeoff, cross-validation, regularization, k-nearest neighbor regression/classification. You should also be able to run and make modifications to basic matlab and python (scikit-learn) code for such algorithms.

Lecture01slides.pdf

Lecture01 Video:

Also available via youtube: https://youtu.be/JF6BugAf0wA

and with subtitles via Canvas Studio here

Reading Assignment (this refers to the book Supervised Machine Learning (Links to an external site.) by Lindholm et al):

Ch 2.1 and 2.2
Ch 3.1 and 3.3
Ch 4.1-4.4
Ch 5.1 and 5.2

Code used in the lecture (please don't worry about the unlogical naming of some of these files...)

crazyflie_matlab.zip - polynomial regression, matlab
lec3_cosinus.ipynb - overfitting and crossvalidation, google colab
lec3_ridge.ipynb - decreasing overfitting problem with ridge regularisation, google colab
knnfig.m - knn, matlab
iris.m - knn on flower example, matlab